Real-time Departure Slotting in Mixed-Mode Operations using Deep Reinforcement Learning: A Case study of Zurich Airport

A mixed-mode runway operation increases the runway capacity by allowing simultaneous arrival and departure operations on the same runway. However, this requires careful evaluation of safe separation by experienced Air Traffic Controllers (ATCOs). In daily operation, ATCOs need to make real-time decisions for departure slotting. However, an increase in runway capacity is not always guaranteed due to stochastic nature of arrivals and departures and associated environmental parameters. To support ATCOs in making real-time departure slotting decision, this paper proposes a Deep Reinforcement Learning approach to suggest departure slot within an incoming stream of arrivals while considering operational constraints and uncertainties. In this work, novel state representation and reward mechanism are designed to facilitate the learning process. Experimentation on A-SMGCS data from Zurich airport shows that the proposed approach achieves the efficiency ratio more than 83.8% of the expected runway capacity while maintaining safe separation distances in mixed-mode operations. The results of this work has demonstrated the potentials of Deep Reinforcement Learning in solving decision-making problems in Air Traffic Management.